Lean back: Songza, ubiquitous listening and Internet music radio for the masses
Bibliographic record
Abstract
Abstract Launched in late 2010, Songza was a small player in the Internet radio universe, available only in the United States and Canada. However, the trade press celebrated Songza for its novel solution to bringing Internet radio to mainstream and profits back to the music industry: ‘lean back’ listening. In June 2014, Google acquired Songza, incorporating its staff, ethos of expert curation, context-sensitive playlists and staunch ‘anti-snobbery’ into Google Play Music, now available in 62 countries and a major competitor in online music. In this article, I investigate how ‘lean back’ listening helped Songza court a wider audience for Internet radio. To do so, I examine how these efforts were framed in the trade press, arguing that Songza’s mass appeal must be understood in relation to histories of domestic ambient music listening, including how it has been devalued and feminized. I also consider the Canadian context, countering how the US context has been generalized in the far from borderless world of streaming music. Ultimately, I argue, Songza’s ‘solutions’ obscured problems involving what happens when music becomes a service; the relation between domesticity, the public and the media; and the place of gender, labour, pleasure and democratic practices in the discussion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".